Childhood overweight and obesity has emerged as an epidemic. In 2003-2004, over 33 percent of US children and adolescents were overweight or obese. As the cohort born since 1980 moves into adulthood and middle age, we will see increasing incidence of diabetes, heart disease, kidney failure, and related metabolic disorders. Considerable research effort has been expended to identify the causes of childhood obesity, a necessary first step in suggesting potential prevention strategies to mitigate or reverse this growing problem. However, to date only particular subsets of the problem have been addressed. Some of the identified factors related to obesity are clearly nested within others. For example, characteristics of the built environment vary by race-ethnicity, and obesogenic factors are more prevalent in disadvantaged communities; this may increase the risk of obesity and low physical activity levels in children. Likewise, metabolic processes are nested within individuals, who are further located within neighborhoods; as a result, insulin resistance may also follow proximity to healthy neighborhoods and their amenities. Disentangling the web of causation of childhood obesity is a formidable task, and both available data and standard epidemiologic analyses may be inadequate to capture multiple and interacting levels of causation. This problem is not particular to obesity epidemiology; the problems of micro- and macro-structure are common in social sciences. Furthermore, the presence of feedback loops precludes standard approaches to causality, which are based on directed (non-feedback) relationships, independence of effects, and identifiability assumptions. In response to RFA-HD-08-023 (Innovative Computational and Statistical Methodologies for the Design and Analysis of Multilevel Studies on Childhood Obesity), we propose a novel multilevel study design and analysis plan to address this problem. In particular, we propose a strategy based on synthesis, simulation, and manipulation. Our methods will focus on a novel methodological technique, agent-based computational modeling.